Physical science is commonly misconceived to be concerned with enduring "hard" data, vastly superior to the evanescent "soft" data of social science. In fact, all the physics experimenter says is "that he feels, or believes, or is of the opinion, that his own work indicates that the [actual value of some parameter] does not depart from his own definitive value by more than so-and-so, meaning thereby, since, he makes no claim to omniscience, that he has found no reason for believing that the departure exceeds that amount" (Dorsey, 1944). Expert judgement is of equal importance in all sciences, social and physical, but the physical sciences usually have a stronger theoretical framework.
There are instances, even in the physical sciences, in which expert judges make ratings on scales. For instance expert judgement is required to determine the quality of a paint's performance as it is tested for resistance, corrosion protection, weathering and the like.
I studied the stain resistance of 42 paint formulations. 5 experts rated each paint's stain resistance on a scale of 0 (complete failure) to 8 (superior performance). There are well-known problems with drawing conclusions directly from such ratings. First, the numerical ratings are not on an interval scale so that comparisons based on "average ratings" have no clear meaning. Second, the judges have different levels of severity in applying the rating scale so that a rating of, say, "3", does not have unique meaning. Third, analysis of variance, the most frequently employed technique, does not even address the main point of the investigation, the quantification of the relative quality of the paints.
Analysis with the Rasch rating scale model, however, directly answered the main research question and opened the way for further investigation. The stain resistance of the paints was measured unambiguously on an interval scale. These linear measures enabled useful conclusions to be drawn with respect to the chemical formulations of the paints. The judges were calibrated with severities which, though clearly different, were then adjusted for, and so prevented from spoiling the measures. Finally, the non-linear structure of the rating scale was determined. These results are discussed in Journal of Coatings Technology, 62, 790, November 1990.
Social science has been mistaken for the poor cousin of physical science for too long. At long last, social scientists can provide guidance to physicists.
Judgement of Stain-Resistance, T Rehfeldt Rasch Measurement Transactions, 1991, 4:4 p. 121
Please help with Standard Dataset 4: Andrich Rating Scale Model
|Rasch Measurement Transactions (free, online)||Rasch Measurement research papers (free, online)||Probabilistic Models for Some Intelligence and Attainment Tests, Georg Rasch||Applying the Rasch Model 3rd. Ed., Bond & Fox||Best Test Design, Wright & Stone|
|Rating Scale Analysis, Wright & Masters||Introduction to Rasch Measurement, E. Smith & R. Smith||Introduction to Many-Facet Rasch Measurement, Thomas Eckes||Invariant Measurement: Using Rasch Models in the Social, Behavioral, and Health Sciences, George Engelhard, Jr.||Statistical Analyses for Language Testers, Rita Green|
|Rasch Models: Foundations, Recent Developments, and Applications, Fischer & Molenaar||Journal of Applied Measurement||Rasch models for measurement, David Andrich||Constructing Measures, Mark Wilson||Rasch Analysis in the Human Sciences, Boone, Stave, Yale|
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